AI Research Scientist - Meta Superintelligence Lab (Technical Leadership)

Meta•Menlo Park, CA

5d•$219,000 - $301,000

About The Position

Meta is seeking Research Scientists to join the Product and Applied Research organization within MSL (Meta Superintelligence Lab). This role requires industry experience working on LLM post-training and the ability to apply relevant AI techniques to build intelligent rich language systems that improve Meta's products and experiences.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
Ph.D. and publications in machine learning, AI, computer science, statistics, applied mathematics, data science, or related technical fields
Proven track record of recent and consistent publication/citation history
Experience leading a team in solving modeling problems using AI/ML approaches
Experience in applying research to production problems
Experience communicating research for public audiences of peers

Nice To Haves

4+ years of experience conducting AI research post PhD
4+ years of experience as a tech lead in leading complex technical projects from end to end
Experience working on frontier-quality/ state-of-the-art Large Language Models
Experience bringing machine learning-based products from research to production
First-author publications at peer-reviewed AI conferences (e.g., NeurIPS, ICML, ICLR, AAAI)

Responsibilities

Help advance the science and technology of intelligent machines
Work on research projects, strategies, and problems of moderate to high complexity and scope. Identify and define both short- and medium-term objectives
Influence progress of relevant research communities by producing publications
Establish strong connection with cross-functional partners and contribute research with the goal of applying to Meta's product development
Lead and collaborate on research projects within a team
Design policies, processes, procedures, methods, tests, and/or components, from the ground up for end-to-end systems
Apply in-depth knowledge of how the machine learning system interacts with the other systems around it